5arXiv cs.LG (Machine Learning)·
Theory of acceptance criteria in speculative decoding for greedy and relaxed regimes
A new arXiv preprint develops a theoretical framework for speculative decoding acceptance criteria beyond the standard stochastic, distribution-preserving setting. The authors characterize rejection regions for greedy decoding, additive/multiplicative relaxed acceptance, top-m criteria, and entropy-thresholded acceptance in terms of KL divergence and margin-based bounds. The framework is extended to greedy tree decoding and validated empirically on Qwen3 models, showing relaxed and tree-based criteria substantially expand certified acceptance regions. The work fills a gap between existing theory and practical inference systems that use non-exact acceptance rules.
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